System and Method for Accommodating Non-Gaussian and Non-Linear Sources of Variation in Statistical Static Timing Analysis

1. A system for statistical timing analysis and optimization of an electrical circuit having two or more digital elements, comprising: at least one parameter input for receiving parameters of the electrical circuit, at least one of the parameters having a non-Gaussian probability distribution; and a statistical static timing analyzer and electrical circuit optimizer for calculating at least one of a signal arrival time and a signal required time for the electrical circuit using the at least one of the parameters having the non-Gaussian probability distribution, and for modifying a component size of the electrical circuit to alter gate timing characteristics of the electrical circuit based upon the at least one of the signal arrival time and the signal required time.

2. The system according to claim 1 , wherein the signal arrival time is one of a minimum signal arrival time and a maximum signal arrival time, and the signal required time is one of a minimum signal required time and a maximum signal required time.

3. The system according to claim 1 , wherein said statistical static timing analyzer calculates the at least one of the signal arrival time and the signal required time by separately integrating over subspaces of parameters with Gaussian probability distributions and subspaces of parameters with non-Gaussian probability distributions.

4. The system according to claim 3 , wherein said statistical static timing analyzer performs integration over the subspaces of parameters with Gaussian probability distributions analytically and over the subspaces of parameters with non-Gaussian probability distributions numerically.

5. The system according to claim 1 , wherein said statistical static timing analyzer calculates the at least one of the signal arrival time by calculating a probability that a signal arrival time A in the electrical circuit is one of greater than and less than a signal arrival time B in the electrical circuit and the signal required time by calculating a probability that a signal required time A in the electrical circuit is one of greater than and less than a signal required time B in the electrical circuit, using the at least one of the parameters having the non-Gaussian probability distribution.

6. The system according to claim 1 , wherein said statistical static timing analyzer calculates the at least one of the signal arrival time and the signal required time by defining a finite region of non-Gaussian parameter variations and building an integration grid in the finite region.

7. The system according to claim 6 , wherein the integration grid has a plurality of cells, and said statistical static timing analyzer calculates a tightness probability, a mean value, and a second moment value for each of the plurality of cells.

8. The system according to claim 7 , wherein the tightness probability, the mean value, and the second moment value are calculated for each of the plurality of cells at a condition wherein the at least one of the parameters having the non-Gaussian probability distribution is fixed to be equal to an average value of the at least one of the parameters in a corresponding one of the plurality of cells.

9. The system according to claim 7 , wherein the tightness probability, the mean value, and the second moment value are calculated analytically.

10. The system according to claim 7 , wherein said statistical static timing analyzer assigns weights to the tightness probability, the mean value, and the second moment value for each of the plurality of cells based on at least respective cell volumes.

11. The system according to claim 7 , wherein said statistical static timing analyzer combines the tightness probability, the mean value, and the second moment value of all of the plurality of cells based on respective cell volumes, an average value of a probability density function of the at least one of the parameters, and at least one of the tightness probability, the mean value, and the second moment value, wherein the at least one of the tightness probability, the mean value, and the second moment value are calculated for a fixed value of the at least one of the parameters.

12. The system according to claim 11 , wherein, for each of the plurality of cells, the at least one of the parameters is fixed to be equal to an average value of the at least one of the parameters in a respective one of the plurality of cells.

13. The system according to claim 6 , wherein the integration grid has a plurality of cells, and said statistical static timing analyzer calculates a tightness probability, a mean value, a variance value, and a second moment value for each of the plurality of cells, and calculates one of an approximate maximum and an approximate minimum of two signal arrival times in a same first-order form based on the tightness probability, the mean value, the variance value, and the second moment value.

14. The system according to claim 13 , wherein the one of the approximate maximum and the approximate minimum of the two signal arrival times is calculated such that the mean value and the variance value thereof are respectively identical to that of one of an exact maximum and an exact minimum of the two signal arrival times.

15. A method for statistical timing analysis and optimization of an electrical circuit having two or more digital elements, comprising the steps of: receiving parameters of the electrical circuit, at least one of the parameters having a non-Gaussian probability distribution; storing at least the at least one of the parameters in a memory: performing a statistical static timing analysis using a processor to calculate at least one of a signal arrival time and a signal required time for the electrical circuit using the at least one of the parameters having the non-Gaussian probability distribution; and modifying a component size of the electrical circuit to alter gate timing characteristics of the electrical circuit based upon the at least one of the signal arrival time and the signal required time.

16. The method according to claim 15 , wherein the signal arrival time is one of a minimum signal arrival time and a maximum signal arrival time and the signal required time is one of a minimum signal required arrival time and a maximum signal required arrival time.

17. The method according to claim 15 , wherein said step of performing the statistical static timing analysis comprises the step of separately integrating over subspaces of parameters with Gaussian probability distributions and subspaces of parameters with non-Gaussian probability distributions.

18. The method according to claim 17 , wherein said integrating step integrates over the subspaces of parameters with Gaussian probability distributions analytically and over the subspaces of parameters with non-Gaussian probability distributions numerically.

19. The method according to claim 15 , wherein said step of performing the statistical static timing analysis comprises the step of calculating a probability that a signal arrival time A in the electrical circuit is one of greater than and less than a signal arrival time B in the electrical circuit, using the at least one of the parameters having the non-Gaussian probability distribution.

20. The method according to claim 15 , wherein said step of performing the statistical static timing analysis comprises the step of defining a finite region of non-Gaussian parameter variations and builds an integration grid in the finite region.

21. The method according to claim 20 , wherein the integration grid has a plurality of cells, and said step of performing the statistical static timing analysis comprises the step of calculating a tightness probability, a mean value, and a second moment value for each of the plurality of cells.

22. The method according to claim 21 , wherein the tightness probability, the mean value, and the second moment value are calculated for each of the plurality of cells at a condition wherein the at least one of the parameters having the non-Gaussian probability distribution is fixed to be equal to an average value of the at least one of the parameters in a corresponding one of the plurality of cells.

23. The method according to claim 21 , wherein the tightness probability, the mean value, and the second moment value are calculated analytically.

24. The method according to claim 21 , wherein said step of performing the statistical static timing analysis further comprises the step of assigning weights to the tightness probability, the mean value, and the second moment value for each of the plurality of cells, based on at least respective cell volumes.

25. The method according to claim 21 , wherein said step of performing the statistical static timing analysis further comprises the step of combining the tightness probability, the mean value, and the second moment value of all of the plurality of cells based on respective cell volumes, an average value of a probability density function of the at least one of the parameters, and at least one of the tightness probability, the mean value, and the second moment value, wherein the at least one of the tightness probability, the mean value, and the second moment value are calculated for a fixed value of the at least one of the parameters.

26. The method according to claim 25 , wherein, for each of the plurality of cells, the at least one of the parameters is fixed to be equal to an average value of the at least one of the parameters in a respective one of the plurality of cells.

27. The method according to claim 20 , wherein the integration grid has a plurality of cells, and said step of performing the statistical static timing analysis further comprises the steps of: calculating a tightness probability, a mean value, a variance value, and a second moment value for each of the plurality of cells; and calculating one of an approximate maximum and an approximate minimum of two signal arrival times in a same first-order form based on the tightness probability, the mean value, the variance value, and the second moment value.

28. The method according to claim 27 , wherein the one of the approximate maximum and the approximate minimum of the two signal arrival times is calculated such that the mean value and the variance value thereof are respectively identical to that of one of an exact maximum and an exact minimum of the two signal arrival times.

29. The method according to claim 15 , wherein the statistical static timing analysis is one of an early mode analysis and a late mode timing analysis.

30. The method according to claim 15 , wherein the at least one of the signal arrival time and the signal required time is one of a rising signal and a falling signal.

31. The method according to claim 15 , wherein the electrical circuit is one of a combinational circuit and a sequential circuit.

32. The method according to claim 15 , wherein the statistical static timing analysis is used to calculate timing slack.

33. The method according to claim 15 , wherein the electrical circuit is one of static and dynamic logic.

34. The method according to claim 15 , wherein the electrical circuit has multiple clock phases.

35. The method according to claim 15 , wherein the parameters of the electrical circuit are one of independent and correlated.

36. The method according to claim 15 , wherein delays in the electrical circuit are one of stored and calculated on the fly.

37. The method according to claim 17 , wherein the integration is one of numerical, analytical and Monte-Carlo.